Data conversion is useful to convert business information from an existing system to another system. The underlying business reasons for the conversion of information range from consolidation of an industry (such as the telecommunications industry) to seeking an upgrade of an information system to gain access to superlative business functionality.
In a typical data conversion project, the first task is mapping the information/ functionality from the source system to the target system. For example, a conversion involving a social security number may comprise a straight mapping, manipulated mapping, and bridge mapping. Straight mapping applies to data that is stored in the target system the same way as it is in the source system. In manipulated mapping, a customer's name may be a one field [whole name] to three field [first name, middle name, and last name] conversion. The data mapping has to split the customer name from the source system into three pieces and put it into three distinct fields in the target system. Other data conversions may require bridging functionality that does not exist in the source system. This requires relatively complex processing where information has to be derived from a combination of other existing sources.
Third party vendors generally perform data conversions from one client's system to another. It is estimated that corporations spend billions of dollars to fund projects to write custom code for converting data from a source system to a target system. Such custom code is generally restricted to a particular conversion and becomes a choice that provides a low return on investment from a re-use perspective. Please see, http://www.campagne.com/dc—info.html and http://www.kinexisdms.com/SAP.htm. Prior art database conversion engines are also customized to the current job and are not reusable without significant rewriting of the code employed on such systems. Therefore, there are no cost-effective tools currently in the market that are built for one-time data conversion needs.
Third party data conversion tools traditionally dump data with no logical relationships. Such dumping often results in a significant percentage of errors, which may or may not be discovered before the system goes live. Alternatively, some tools (see, U.S. Pat. No. 6,151,608 issued Oct. 19, 1999, to Morgenstern; DataJunction, http://www.datajunction.com; and Evolutionary Technologies International) are appended to a source system to provide customized, real-time, data conversion into the target system but these do not provide generic, one-time, bulk data conversion solutions. Other prior art systems provide engines for migrating data from one specific database to another specific database (i.e., relational database to relational database as per U.S. Pat. No. 6,151,608 (issued Nov. 21, 2000, to Abrams); network database to relational database as per U.S. Pat. No. 5,930,806 (issued Jul. 27, 1999, to Taira); and relational database to object-oriented database as per U.S. Pat. No. 5,732,257 (issued Mar. 24, 1998, to Atkinson). None of these engines, however, provide a generic solution for effecting the conversion of any combination of source and target databases. These engines are also geared toward real-time translation rather than one-time conversions. Some of the prior art systems, discussed above, further provide inflexible mapping templates for effecting the conversion rather than configurable rules to broadly address any type of conversion needed.
Bulk conversions and/or one-time solutions for generic data conversions present significantly different performance and cost issues than real-time customized solutions. Therefore, the prior art does not provide a system and method, which effectively answers these needs.